Hierarchical Error Evaluation: The Role of Medial-Frontal Cortex in Postural Control
Bibliographic record
Abstract
Motor error evaluation appears to be a hierarchically organized process subserved by 2 distinct systems: a higher level system within medial-frontal cortex responsible for movement outcome evaluation (high-level error evaluation) and a lower level posterior system(s) responsible for the mediation of within-movement errors (low-level error evaluation). While a growing body of evidence suggests that a reinforcement learning system within medial-frontal cortex plays a crucial role in the evaluation of high-level errors made during discrete reaching movements and continuous motor tracking, the role of this system in postural control is currently unclear. Participants learned a postural control task via a feedback-driven trial-and-error shaping process. In line with previous findings, electroencephalographic recordings revealed that feedback about movement outcomes elicited a feedback error-related negativity: a component of the human event-related brain potential associated with high-level outcome evaluation within medial-frontal cortex. Thus, the data provide evidence that a high-level error-evaluation system within medial-frontal cortex plays a key role in learning to control our body posture.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".